R1 0528 vs Gemma 4 31B

Across our 12-test suite, the pair ties on 8 tasks, R1 wins 2, and Gemma wins 2 (no model wins a majority). Detailed test-by-test:

• long_context: R1 0528 = 5 vs Gemma 4 31B = 4 — R1 wins in our testing and ranks tied for 1st (rank 1 of 55, tied with 36) while Gemma ranks 38 of 55. This means R1 is measurably better for retrieval and accuracy over 30K+ token contexts.
• safety_calibration: R1 = 4 vs Gemma = 2 — R1 ranks 6/55 (4-model tie); Gemma ranks 12/55. R1 is more likely to refuse harmful requests and better calibrate permissive ones in our tests.
• structured_output: Gemma = 5 vs R1 = 4 — Gemma ranks tied for 1st (1 of 54) while R1 ranks 26 of 54. Gemma is stronger at JSON/schema compliance and format adherence. Note: R1 has a known quirk of returning empty responses on structured_output in some cases.
• strategic_analysis: Gemma = 5 vs R1 = 4 — Gemma ranks tied for 1st (1 of 54); R1 ranks 27 of 54. For nuanced tradeoff reasoning with numbers, Gemma outperforms in our testing.
• tool_calling: both = 5 and tied for 1st — both models perform at the top of our suite for function selection and argument accuracy.
• faithful, classification, persona_consistency, agentic_planning, multilingual, constrained_rewriting, creative_problem_solving: these are ties in our testing (scores generally 4–5). For example, both score 5 on faithfulness and persona_consistency and are tied for 1st on several of those ranks.
• external math benchmarks: R1 0528 posts 96.6% on MATH Level 5 and 66.4% on AIME 2025 (Epoch AI) — include these as supplementary evidence for strong quantitative capability; Gemma 4 31B has no external math scores in the payload. Summary: Gemma is better for structured outputs and numeric/strategic reasoning at lower cost; R1 is better for long-document work and safety-sensitive tasks.

Per-mTok pricing (input/output): R1 0528 = $0.50/$2.15; Gemma 4 31B = $0.13/$0.38. Assuming a 50/50 input-output split, 1M tokens (500k in / 500k out) costs: R1 = $1,325 and Gemma = $255. At 10M tokens: R1 = $13,250 vs Gemma = $2,550. At 100M tokens: R1 = $132,500 vs Gemma = $25,500. The price ratio in the payload is ~5.66x; high-volume apps, narrow-margin products, and consumer-facing chat services will be most impacted by this gap. Teams that require R1’s specific strengths (long_context and safety) should budget for the higher spend; cost-sensitive projects should prefer Gemma for equivalent performance across most other tasks.

Choose R1 0528 if:

• You need the best long-context retrieval and accuracy (R1 scores 5 vs Gemma 4).
• Safety calibration matters (R1 4 vs Gemma 2).
• You can accept higher pricing (R1 output $2.15/mTok) and handle R1’s quirks (uses reasoning tokens, may require large max completion tokens). Choose Gemma 4 31B if:
• You need reliable structured outputs/JSON and strategic numeric reasoning (Gemma scores 5 on both).
• Budget and per-token cost are a priority (Gemma input/output $0.13/$0.38 vs R1 $0.50/$2.15).
• You want multimodal input support and a very large context window (Gemma’s context_window=262,144 tokens).